Title
Abrupt-motion-aware lightweight visual tracking for unmanned aerial vehicles
Abstract
Visual tracking for unmanned aerial vehicles (UAVs) is a hot research topic for the wide applications of UAVs. As UAVs are high-altitude and high-freedom platforms, small targets in UAV tracking sequences are often under the attribute of large-scale location change due to the abrupt motion of the platform. Currently, many visual tracking methods based on the local search hypothesis have been widely researched on low-speed moving platforms. However, these methods cannot be directly used on the UAV platform, because targets will appear in any position of the new frame. To address this problem, we propose an abrupt-motion-aware visual tracking method in this paper. Because of the high power consumption of deep learning models, the proposed method is a lightweight tracker for small UAVs without the deep learning framework. Our method consists of three major components: abrupt motion estimation, object tracking and model updating. Abrupt motion often leads to abnormal changes in the response map of trackers. Thus, by analyzing the changes of tracking response maps, the abrupt motion can be detected efficiently. When abrupt motion happens, keypoint matching will be adaptively implemented to estimate the ego-motion and skipped otherwise. Then, the target location is predicted by the correlation filter tracker in a local search region. Moreover, according to the confidence analysis, an adaptive model update strategy is designed to alleviate the model noise caused by the short-term occlusion. Experimental results confirm the robustness and the accuracy of our method on challenging sequences and show the comparative performance of the proposed method against several state-of-the-art lightweight methods.
Year
DOI
Venue
2021
10.1007/s00371-020-01805-9
The Visual Computer
Keywords
DocType
Volume
Abrupt motion estimation, Object tracking, Keypoint matching, Correlation filter
Journal
37
Issue
ISSN
Citations 
2
0178-2789
1
PageRank 
References 
Authors
0.36
15
4
Name
Order
Citations
PageRank
Kaicheng Gong110.36
Zhiguo Cao231444.17
Yang Xiao323726.58
Zhiwen Fang451.10